/**
 * This program is free software, you can redistribute it and/or modify it.
 * Copyright (c) 2025 Huawei Technologies Co., Ltd.
 * This file is a part of the CANN Open Software.
 * Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
 * Please refer to the License for details. You may not use this file except in compliance with the License.
 * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING
 * BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
 * See LICENSE in the root of the software repository for the full text of the License.
 */

/*!
 * \file aclnn_flatten.cpp
 * \brief
 */
#include "aclnn_flatten.h"
#include "flatten.h"
#include "aclnn_kernels/reshape.h"
#include "aclnn_kernels/cast.h"
#include "aclnn_kernels/contiguous.h"
#include "aclnn_kernels/common/op_error_check.h"
#include "common/op_api_def.h"
#include "opdev/format_utils.h"
#include "opdev/op_dfx.h"
#include "opdev/op_executor.h"
#include "opdev/tensor_view_utils.h"
#include "opdev/platform.h"

using namespace op;

#ifdef __cplusplus
extern "C" {
#endif

static constexpr int64_t MAX_DIM_LEN = 8;

// 根据API定义，需要列出所能支持的所有dtype
static const std::initializer_list<op::DataType> Ascend910_DTYPE_SUPPORT_LIST = {
    op::DataType::DT_FLOAT,  op::DataType::DT_FLOAT16, op::DataType::DT_INT32, op::DataType::DT_INT8,
    op::DataType::DT_UINT8,  op::DataType::DT_INT64,   op::DataType::DT_INT16, op::DataType::DT_UINT16,
    op::DataType::DT_UINT32, op::DataType::DT_UINT64,  op::DataType::DT_BOOL};

static const std::initializer_list<op::DataType> Ascend910B_DTYPE_SUPPORT_LIST = {
    op::DataType::DT_FLOAT,  op::DataType::DT_FLOAT16, op::DataType::DT_INT32, op::DataType::DT_INT8,
    op::DataType::DT_UINT8,  op::DataType::DT_INT64,   op::DataType::DT_INT16, op::DataType::DT_UINT16,
    op::DataType::DT_UINT32, op::DataType::DT_UINT64,  op::DataType::DT_BOOL,  op::DataType::DT_BF16};

static inline int64_t MakeWrapDim(int64_t dim, int64_t dimPostExpr)
{
    // 支持0维tensor
    if (dimPostExpr <= 0) {
        dimPostExpr = 1;
    }
    if (dim < 0) {
        dim += dimPostExpr;
    }
    return dim;
}

static Shape GetOutShape(const aclTensor* self, const int64_t axis)
{
    // self的dimNum为0时，axis只能为0，out的shape为[1,1];
    // self的dimNum为1时，axis只能为0，out的shape为[1,self.GetDim(0)]。
    Shape outShape;
    int64_t dim0 = 1;
    int64_t dim1 = 1;
    size_t selfDim = self->GetViewShape().GetDimNum();
    for (int64_t i = 0; i < axis; i++) {
        dim0 *= self->GetViewShape().GetDim(i);
    }
    for (size_t i = axis; i < selfDim; i++) {
        dim1 *= self->GetViewShape().GetDim(i);
    }
    outShape.AppendDim(dim0);
    outShape.AppendDim(dim1);
    return outShape;
}

static bool CheckNotNull(const aclTensor* self, const aclTensor* out)
{
    // self、index、out不能为空指针
    OP_CHECK_NULL(self, return false);
    OP_CHECK_NULL(out, return false);
    return true;
}

static bool CheckDtypeValid(const aclTensor* self, const aclTensor* out)
{
    // self和out数据类型必须一样
    OP_CHECK_DTYPE_NOT_SAME(self, out, return false);

    // 获取芯片类型,判断是1971还是1980
    bool is910bSocVersion =
        (GetCurrentPlatformInfo().GetSocVersion() == SocVersion::ASCEND910B ||
         GetCurrentPlatformInfo().GetSocVersion() == SocVersion::ASCEND910_93 ||
         GetCurrentPlatformInfo().GetSocVersion() == SocVersion::ASCEND910_95);
    const std::initializer_list<DataType> DTYPE_SUPPORT_LIST_CURRENT =
        is910bSocVersion ? Ascend910B_DTYPE_SUPPORT_LIST : Ascend910_DTYPE_SUPPORT_LIST;

    // 检查self的数据类型是否在支持列表内
    OP_CHECK_DTYPE_NOT_SUPPORT(self, DTYPE_SUPPORT_LIST_CURRENT, return false);

    return true;
}

static bool CheckShape(const aclTensor* self, const int64_t axis, const aclTensor* out)
{
    // self的dim小于8维
    OP_CHECK_MAX_DIM(self, MAX_SUPPORT_DIMS_NUMS, return false);

    // self的维度需要大于axis取正之后的值
    size_t selfDim = self->GetViewShape().GetDimNum();
    int64_t axisWrap = MakeWrapDim(axis, static_cast<int64_t>(selfDim));
    // 如果self的dim为0或者1，则axis只能取0。
    if (selfDim <= 1 && axisWrap > 0) {
        OP_LOGE(
            ACLNN_ERR_PARAM_INVALID, "Dimension out of range (expected to be in range of [-1, 0], but got %ld)",
            axisWrap);
        return false;
    }
    // 其他情况则
    if ((axisWrap < 0) || (selfDim > 1 && static_cast<int64_t>(selfDim) <= axisWrap)) {
        OP_LOGE(
            ACLNN_ERR_PARAM_INVALID, "Dimension out of range (expected to be in range of [-%zu, %zu], but got %ld)",
            selfDim, selfDim - 1, axis);
        return false;
    }

    // 根据算子语义，推导算子输出shape
    Shape outShape = GetOutShape(self, axisWrap);
    // 判断out的shape与推导出的输出shape是否相等
    OP_CHECK_SHAPE_NOT_EQUAL_WITH_EXPECTED_SIZE(out, outShape, return false);

    return true;
}

static aclnnStatus CheckParams(const aclTensor* self, const int64_t axis, const aclTensor* out)
{
    // 1. 检查参数是否为空指针
    CHECK_RET(CheckNotNull(self, out), ACLNN_ERR_PARAM_NULLPTR);

    // 2. 检查输入的数据类型是否在API支持的数据类型范围之内，需要根据api定义校验
    CHECK_RET(CheckDtypeValid(self, out), ACLNN_ERR_PARAM_INVALID);

    // 3. 检查shape是否满足约束
    CHECK_RET(CheckShape(self, axis, out), ACLNN_ERR_PARAM_INVALID);

    return ACLNN_SUCCESS;
}

aclnnStatus aclnnFlattenGetWorkspaceSize(
    const aclTensor* self, int64_t axis, aclTensor* out, uint64_t* workspaceSize, aclOpExecutor** executor)
{
    L2_DFX_PHASE_1(aclnnFlatten, DFX_IN(self, axis), DFX_OUT(out));
    // 固定写法，创建OpExecutor
    auto uniqueExecutor = CREATE_EXECUTOR();
    CHECK_RET(uniqueExecutor.get() != nullptr, ACLNN_ERR_INNER_CREATE_EXECUTOR);

    // 固定写法，参数检查
    auto ret = CheckParams(self, axis, out);
    CHECK_RET(ret == ACLNN_SUCCESS, ret);

    // 算子的空tensor的支持情况
    if (self->IsEmpty() || out->IsEmpty()) {
        // 根据实际支持情况补充
        *workspaceSize = 0;
        uniqueExecutor.ReleaseTo(executor);
        return ACLNN_SUCCESS;
    }

    // axis取正
    int64_t axisWrap = MakeWrapDim(axis, static_cast<int64_t>(self->GetViewShape().GetDimNum()));

    // self如果非连续，需要转连续
    auto selfContiguous = l0op::Contiguous(self, uniqueExecutor.get());
    CHECK_RET(selfContiguous != nullptr, ACLNN_ERR_INNER_NULLPTR);

    if (GetCurrentPlatformInfo().GetSocVersion() == SocVersion::ASCEND910_95) {
        auto selfReshape = l0op::Reshape(selfContiguous, out->GetViewShape(), uniqueExecutor.get());
        CHECK_RET(selfReshape != nullptr, ACLNN_ERR_INNER_NULLPTR);

        // 如果出参out是非连续Tensor，需要把计算完的连续Tensor转非连续
        auto viewCopyResult = l0op::ViewCopy(selfReshape, out, uniqueExecutor.get());
        CHECK_RET(viewCopyResult != nullptr, ACLNN_ERR_INNER_NULLPTR);
    } else {
        // 调用l0算子Flatten进行计算
        auto flattenResult = l0op::Flatten(selfContiguous, uniqueExecutor.get(), axisWrap);
        CHECK_RET(flattenResult != nullptr, ACLNN_ERR_INNER_NULLPTR);

        // 如果出参out是非连续Tensor，需要把计算完的连续Tensor转非连续
        auto viewCopyResult = l0op::ViewCopy(flattenResult, out, uniqueExecutor.get());
        CHECK_RET(viewCopyResult != nullptr, ACLNN_ERR_INNER_NULLPTR);
    }

    // 固定写法，获取计算过程中需要使用的workspace大小
    *workspaceSize = uniqueExecutor->GetWorkspaceSize();
    uniqueExecutor.ReleaseTo(executor);
    return ACLNN_SUCCESS;
}

aclnnStatus aclnnFlatten(void* workspace, uint64_t workspaceSize, aclOpExecutor* executor, const aclrtStream stream)
{
    L2_DFX_PHASE_2(aclnnFlatten);
    // 固定写法，调用框架能力，完成计算
    return CommonOpExecutorRun(workspace, workspaceSize, executor, stream);
}

#ifdef __cplusplus
}
#endif
